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2020 | OriginalPaper | Buchkapitel

15. Die Anwendung von Machine Learning zur Gewinnung von Erkenntnissen aus Dokumentenstapeln

verfasst von : Stefan Ebener

Erschienen in: Künstliche Intelligenz in Wirtschaft & Gesellschaft

Verlag: Springer Fachmedien Wiesbaden

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Zusammenfassung

„Document Understanding“ ist das tiefe Verständnis eines Textes. Im Kern geht es um die Konvertierung von unstrukturierten Daten in Informationen und für Unternehmen gleichermaßen um die die Einhaltung von Governance- und Compliance-Richtlinien. Zum Einsatz kommt zumeist eine Sammlung von verschiedenen Methoden, zu denen unter anderem die Document Classification oder auch die Entity Extraction gehören. Viele Ansätze beruhen auf regelbasierten Systemen respektive auf statistischen Verfahren.

Der Einsatz von Machine Learning zur massenhaften Erschließung unstrukturierter Dokumente eröffnet neue Wege, um unter anderem Beziehungen zwischen Dokumenten sichtbar zu machen. ML ermöglicht Vorhersagen zur Dokumentenklassifizierung oder etwa die Extraktion von Wissen aus Textpassagen, Grafiken oder Feldern jenseits einfacher Mustererkennung. ML stellt Möglichkeiten einer semantischen Suche über Dokumente hinweg zur Verfügung und legt den Grundstein für erweiterte Analysen beispielsweise der Anomalieerkennung.

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Kurzbezeichnung für einen Trainingsdatensatz bereitgestellt durch die Message Understanding Conferences.

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Vielfach erfolgt eine Transkription für Audio- und Video-Daten durch das entsprechende System automatisch.

https://github.com/cayleygraph/cayley

https://patents.google.com/patent/US20170011116A1/en

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Titel: Die Anwendung von Machine Learning zur Gewinnung von Erkenntnissen aus Dokumentenstapeln
verfasst von: Stefan Ebener
Verlag: Springer Fachmedien Wiesbaden
Buch: Künstliche Intelligenz in Wirtschaft & Gesellschaft
Print ISBN: 978-3-658-29549-3

Electronic ISBN: 978-3-658-29550-9

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-658-29550-9_15